Super-resolution reconstruction technology has important scientific significance and application value in the field of image processing by performing image restoration processing on one or more low-resolution images to improve image spatial resolution. Based on the SCSR algorithm and VDSR network, in order to further improve the image reconstruction quality, an image super-resolution reconstruction algorithm combined with multi-residual network and multi-feature SCSR(MRMFSCSR) is proposed. Firstly, at the sparse reconstruction stage, according to the characteristics of image blocks, our algorithm extracts the contour features of non-flat blocks by NSCT transform, extracts the texture features of flat blocks by Gabor transform, then obtains the reconstructed high-resolution (HR) images by using sparse models. Secondly, according to improve the VDSR deep network and introduce the feature fusion idea, the multi-residual network structure (MR) is designed. The reconstructed HR image obtained by the sparse reconstruction stage is used as the input of the MR network structure to optimize the high-frequency detail residual information. Finally, we can obtain a higher quality super-resolution image compared with the SCSR algorithm and the VDSR algorithm.

通过对一个或多个低分辨率图像进行图像恢复处理以提高图像空间分辨率，超分辨率重建技术在图像处理领域具有重要的科学意义和应用价值。在SCSR算法和VDSR网络的基础上，为进一步提高图像重建质量，提出了一种结合多残差网络和多特征SCSR的图像超分辨率重建算法。首先，在稀疏重建阶段，根据图像块的特征，我们的算法通过NSCT变换提取非平坦块的轮廓特征，通过Gabor变换提取平坦块的纹理特征，然后获得重建的高分辨率（ HR）图像使用稀疏模型。其次，为了完善VDSR深层网络并引入特征融合的思想，设计了多残留网络结构（MR）。通过稀疏重建阶段获得的重建HR图像被用作MR网络结构的输入，以优化高频细节残差信息。最后，与SCSR算法和VDSR算法相比，我们可以获得更高质量的超分辨率图像。